With the rapid development of internet, the user scale of online shopping in China is increasing continuously. In 2010, the transaction scale of online shopping market in China is close to 500 billion, reaching up to 498 billion and accounting for 3.2% of the total volume of retail sales of social consumer goods; meanwhile, the user scale of online shopping reaches up to 1.48 hundred million, and has a penetration rate of 30.8% in netizen. For some traditional enterprises, it is already very difficult to incur any major changes to the current market through some traditional marketing means. If there is intention to open the distribution channels of the enterprise completely, new concepts and new method must be introduced for the enterprise. Online shopping has just provided a very good opportunity and platform for current traditional enterprises. By way of a third-party platform and by establishing a self-possessed platform, the traditional enterprises are testing online shopping one after another. Establishing a reasonable online shopping platform, integrating channels and perfecting industrial layouts have become the focuses and outlets for the development of traditional enterprises in the future.
With the stampeding rise of online shopping platforms, how to analyze and collect user interest data based on big data so as to better provide recommendation for the user and to improve user's experiences has become the focus of research.
Chinese patent application No. 201310717507.4 (entitled “information individualized recommendation method based on Web log data”) describes a Web recommendation technology using log analysis. In this patent, by analyzing and pre-processing data of Web log files in a server, clean, regular and accurate data source is extracted; a user interest matrix is established by using collaborative filtering technology, the degree of similarity between individual users is calculated, and the users having larger degrees of similarity are selected as similar users; a recommendation resource pool is established for the interests and hobbies of similar users; the server selects a page in the recommendation resource pool whose recommendation value is greater than a threshold value and recommends it to the user.
However, in this patent, the measurement of user's interest is the time which the user spends in browsing a certain resource classification. The interest granularity acquired by analysis is largely dependent on the thickness granularity of resource classification. If the granularity is thicker, it is difficult to accurately determine fine tendency of interest. Moreover, the pages browsed by the user typically cover a plurality of classification subjects. Page data sources of the plurality of subjects will result in an inaccurate final analysis result.